From CFTR to a CF signalling network: a systems biology approach to study Cystic Fibrosis

Abstract

Background: Cystic Fibrosis (CF) is a monogenic disease caused by mutations in the gene coding the Cystic Fibrosis Transmembrane Regulator (CFTR) protein, but its overall physio-pathology cannot be solely explained by the loss of the CFTR chloride channel function. Indeed, CFTR belongs to a yet not fully deciphered network of proteins participating in various signalling pathways.

Methods: We propose a systems biology approach to study how the absence of the CFTR protein at the membrane leads to perturbation of these pathways, resulting in a panel of deleterious CF cellular phenotypes.

Results: Based on publicly available transcriptomic datasets, we built and analyzed a CF network that recapitulates signalling dysregulations. The CF network topology and its resulting phenotypes were found to be consistent with CF pathology.

Conclusion: Analysis of the network topology highlighted a few proteins that may initiate the propagation of dysregulations, those that trigger CF cellular phenotypes, and suggested several candidate therapeutic targets. Although our research is focused on CF, the global approach proposed in the present paper could also be followed to study other rare monogenic diseases.

Keywords: CF cellular phenotypes; CF signalling network; Cystic Fibrosis; Network topology; Therapeutic target.

Fig. 1

Global approach followed to build the CF network: A meta-analysis of CF transcriptomic data allowed the identification of dysregulated pathways and the construction of the corresponding CF network. This network comprises known CFTR interactors that can be viewed as source nodes initiating the propagation of dysregulations

Fig. 2

Heatmap of the GSEA Normalized Enrichment Scores (NES) of the biological pathways differentially expressed in at least 3 studies. The datasets can be clustered in two subgroups based on their NES: Subgroup 1 and Subgroup 2, respectively in agreement and in contradiction with CF physio-pathology. Black boxes around the tiles represent the pathways significantly differentially expressed in the corresponding dataset

Fig. 3

The CF network. (A) The main component comprises 317 nodes connected by 517 interactions and two small unconnected components shown in (B): the two unconnected components correspond to the TGF$\beta$ and the JAK-STAT signalling pathways. The cellular phenotypes triggered by the sink nodes of the two components are surrounded by black contours

Fig. 4

CFTR interactors in the CF network: Known protein-protein interactions involving CFTR interactors in CFTR PPI

Fig. 5

Illustration of propagation of dysregulation and remarkable nodes in the CF network: the source nodes (orange disks) are wt-CFTR interactors (that do not interact with F508del-CFTR) or connected to wt-CFTR interactors via a single intermediate protein (magenta circles). Nodes with high betweenness centrality (purple disks) are proteins through which much information flows within the network. Sink nodes (blue triangles) modulate their corresponding phenotypes

Fig. 6

Extract from the CF network showing the TRADD protein connected to the TNF-$\alpha$ signalling pathway, and to 5 other sink nodes, including FOS and JUN which form the AP-1 transcription factor, downstream of the MAPK cascade. The cellular phenotypes triggered by the sink nodes are surrounded by black contours. Note that TRADD is connected to the 35 sink nodes, but only part of the nodes downstream of TRADD in the network are represented

Fig. 7

A Histogram of the betweenness centrality measures for all nodes in the CF signalling network; (B) Number of sink nodes to which each of the 8 source nodes are connected

Fig. 8

Subnetworks of the CF network illustrating the connections between the source nodes TRADD, SYK, PLCB1/3, and CSNK2A1 and the sink node NFKB1